import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)

/hpc/group/pbenfeylab/ch416/miniconda3/envs/genesys/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

## Conda Env pytorch-gpu on DCC
print(torch.__version__)
print(sc.__version__) 

1.11.0
1.9.6

## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data

with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
dataset = Root_Dataset(data['X_test'], data['y_test'])
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)

input_size = data['X_train'].shape[1]
## 10 cell types 
output_size = 10
embedding_dim = 256
hidden_dim = 256
n_layers = 2
device = "cpu"
path = "./"

Load trained GeneSys model

model = ClassifierLSTM(input_size, output_size, embedding_dim, hidden_dim, n_layers).to(device)
model.load_state_dict(torch.load(path+"/workstation/genesys_model_trained_on_root_atlas_20240308_continue4.pth", map_location=torch.device('cpu')))
model = model
model.eval()

ClassifierLSTM(
  (fc1): Sequential(
    (0): Linear(in_features=17513, out_features=256, bias=True)
    (1): Dropout(p=0.2, inplace=False)
    (2): GaussianNoise()
  )
  (fc): Sequential(
    (0): ReLU()
    (1): Linear(in_features=512, out_features=512, bias=True)
    (2): ReLU()
    (3): Linear(in_features=512, out_features=10, bias=True)
  )
  (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.2, bidirectional=True)
  (dropout): Dropout(p=0.2, inplace=False)
  (b_to_z): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (bz2_infer_z1): DBlock(
    (fc1): Linear(in_features=1024, out_features=256, bias=True)
    (fc2): Linear(in_features=1024, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z1_to_z2): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z_to_x): Decoder(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=256, out_features=256, bias=True)
    (fc3): Linear(in_features=256, out_features=17513, bias=True)
  )
)

classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}

cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000

## GRN for the transition t7 to t9
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            pred_h = model.init_hidden(batch_size)
            tfrom = model.generate_next(x, pred_h, 6).to('cpu').detach().numpy()
            cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            
            pred_h = model.init_hidden(batch_size)
            tto = model.generate_next(x, pred_h, 8).to('cpu').detach().numpy()   
            cto = tto[np.where(np.array(y_label)==ct)[0],:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast
Trichoblast
Cortex
Endodermis
Pericycle
Procambium
Xylem
Phloem
Lateral Root Cap
Columella

# Save the array to disk
np.save('genesys_ctw_t7-t9.npy', ctw)

ctw = np.load('genesys_ctw_t7-t9.npy')

## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])

## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list

wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")

## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"

pd.Series(wanted_TFs['Name']).value_counts().head(5)

Name
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: count, dtype: int64

Network analysis

TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))

def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)

atri = network(0)

tri = network(1)

cor = network(2)

end = network(3)

per = network(4)

pro = network(5)

xyl = network(6)

phl = network(7)

lrc = network(8)

col = network(9)

atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']

## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts())
tf_occurance = tf_occurance.rename(columns={
    'count': 'tf_occurance'
})
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)

## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)

	atri_betweenness_centrality	tri_betweenness_centrality	lrc_betweenness_centrality	atri_out_centrality	tri_out_centrality	lrc_out_centrality	atri_in_centrality	tri_in_centrality	lrc_in_centrality	centrality_count	centrality_sum

## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)

	atri_betweenness_centrality	tri_betweenness_centrality	atri_out_centrality	tri_out_centrality	atri_in_centrality	tri_in_centrality	centrality_count	centrality_sum
NAC6	0.979472	0.000935	0.449477	0.017065	0.477352	0.143345	6	8.067646
WRKY61	0.799347	0.074419	0.376307	0.515358	0.118467	0.156997	6	8.040895
AT2G28710	0.792829	0.000561	0.418118	0.010239	0.313589	0.061433	6	7.596770

## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	atri_betweenness_centrality	atri_out_centrality	atri_in_centrality	centrality_count	centrality_sum
GL2	0.972686	0.191638	0.142857	3	4.307181
MYB23	0.911381	0.062718	0.313589	3	4.287688
TTG2	0.908019	0.073171	0.198606	3	4.179796
AT1G14600	0.901087	0.034843	0.261324	3	4.197254
MC2	0.865963	0.198606	0.209059	3	4.273629
AT3G05860	0.711752	0.386760	0.456446	3	4.554957
bZIP2	0.555201	0.031359	0.066202	3	3.652762
AT5G22890	0.429875	0.271777	0.209059	3	3.910711
WRKY13	0.426573	0.076655	0.327526	3	3.830755
AT3G04850	0.220999	0.038328	0.257840	3	3.517166
PAT1	0.100485	0.034843	0.052265	3	3.187593
RMR1	0.026303	0.275261	0.031359	3	3.332923
AT4G01350	0.013547	0.069686	0.083624	3	3.166858
KAN	0.010916	0.205575	0.083624	3	3.300115
MEA	0.005373	0.153310	0.181185	3	3.339867
GRF3	0.003996	0.045296	0.250871	3	3.300163
ULT2	0.001681	0.038328	0.048780	3	3.088789
NAC084	0.001584	0.006969	0.101045	3	3.109598
HB24	0.001340	0.149826	0.243902	3	3.395068
NPH4	0.000670	0.010453	0.027875	3	3.038998
OFP18	0.000463	0.097561	0.135889	3	3.233912
WRKY54	0.000426	0.052265	0.076655	3	3.129346
AT5G26749	0.000317	0.048780	0.062718	3	3.111815

## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	tri_betweenness_centrality	tri_out_centrality	tri_in_centrality	centrality_count	centrality_sum
LRL3	0.979160	0.941980	0.802048	3	5.723187
AT4G09100	0.978470	0.655290	0.341297	3	4.975057
RSL2	0.963100	0.795222	0.245734	3	5.004056
RSL4	0.912619	0.610922	0.064846	3	4.588387
AT5G56200	0.865772	0.238908	0.102389	3	4.207069
AT3G53370	0.840631	0.211604	0.081911	3	4.134146
RHD6	0.816518	0.679181	0.153584	3	4.649282
IAA31	0.803672	0.351536	0.064846	3	4.220055
RAP2.11	0.699320	0.238908	0.105802	3	4.044030
AT5G06800	0.133737	0.447099	0.037543	3	3.618379
EIL2	0.117151	0.075085	0.010239	3	3.202476
AT2G20030	0.011618	0.105802	0.013652	3	3.131072
HB16	0.006826	0.075085	0.150171	3	3.232082
AT2G14760	0.004009	0.010239	0.020478	3	3.034726
AT2G05160	0.003787	0.296928	0.040956	3	3.341671
CIP8	0.003378	0.006826	0.034130	3	3.044334
AT4G39160	0.000257	0.047782	0.092150	3	3.140189
AT3G24820	0.000012	0.010239	0.064846	3	3.075097

## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	lrc_betweenness_centrality	lrc_out_centrality	lrc_in_centrality	centrality_count	centrality_sum
GATA2	0.983314	0.092105	0.655702	3	4.731121
OFP6	0.931213	0.214912	0.149123	3	4.295248
BRON	0.791170	0.050439	0.050439	3	3.892047
BNQ3	0.737531	0.160088	0.116228	3	4.013847
AT1G05805	0.616806	0.063596	0.089912	3	3.770315
AT1G68920	0.610762	0.039474	0.140351	3	3.790587
BZIP34	0.587666	0.072368	0.063596	3	3.723631
PYE	0.587459	0.024123	0.081140	3	3.692722
AT1G74840	0.410044	0.024123	0.105263	3	3.539430
ATS	0.372373	0.017544	0.135965	3	3.525882
AT1G09060	0.017250	0.028509	0.030702	3	3.076460
AT1G69030	0.006232	0.021930	0.065789	3	3.093951
HAM3	0.004482	0.111842	0.054825	3	3.171149
WRI4	0.001668	0.024123	0.059211	3	3.085001
AT4G13040	0.001552	0.072368	0.015351	3	3.089271
AT1G09250	0.000361	0.059211	0.089912	3	3.149484
ARF6	0.000005	0.019737	0.118421	3	3.138163
WRKY3	0.000005	0.030702	0.043860	3	3.074566
AT2G29065	0.000005	0.006579	0.043860	3	3.050443

## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	col_betweenness_centrality	col_out_centrality	col_in_centrality	centrality_count	centrality_sum
NTT	0.921259	0.120209	0.165505	3	4.206974
IAA20	0.861707	0.439024	0.681185	3	4.981916
SCL1	0.831716	0.181185	0.048780	3	4.061681
ASL1	0.730363	0.198606	0.043554	3	3.972524
AT3G25790	0.633806	0.099303	0.060976	3	3.794085
AT2G20110	0.511119	0.033101	0.041812	3	3.586032
BPC7	0.472889	0.003484	0.024390	3	3.500763
SPT	0.178202	0.022648	0.036585	3	3.237435
AIL5	0.149561	0.006969	0.060976	3	3.217506
CHR17	0.121367	0.047038	0.045296	3	3.213702
SIGE	0.105886	0.029617	0.108014	3	3.243516
RR1	0.097211	0.054007	0.054007	3	3.205225
APRR8	0.078194	0.027875	0.033101	3	3.139169
AT2G32030	0.050891	0.033101	0.067944	3	3.151936
AT1G29950	0.044205	0.008711	0.026132	3	3.079048
AT5G23405	0.043657	0.168990	0.057491	3	3.270138
CHR11	0.031429	0.108014	0.060976	3	3.200418
AT3G21810	0.029483	0.022648	0.024390	3	3.076521
ARF10	0.013478	0.198606	0.156794	3	3.368879
AGL21	0.007717	0.052265	0.034843	3	3.094825
NTM1	0.004734	0.090592	0.036585	3	3.131912
HSL1	0.004214	0.132404	0.132404	3	3.269022
LZF1	0.003487	0.006969	0.050523	3	3.060979
ATRX	0.002347	0.069686	0.064460	3	3.136494
AP2	0.002204	0.031359	0.059233	3	3.092797
SR1	0.001700	0.041812	0.092334	3	3.135846
AT5G65490	0.001587	0.024390	0.036585	3	3.062563
BBX28	0.001368	0.027875	0.080139	3	3.109382
LUG	0.001101	0.064460	0.057491	3	3.123052
SPL14	0.000493	0.055749	0.109756	3	3.165998
NAC016	0.000234	0.026132	0.034843	3	3.061210
AT5G06110	0.000158	0.066202	0.073171	3	3.139531
SYD	0.000131	0.048780	0.064460	3	3.113371
PRR7	0.000088	0.083624	0.015679	3	3.099391
PC-MYB1	0.000085	0.045296	0.073171	3	3.118552
DOT2	0.000030	0.043554	0.101045	3	3.144630
AT2G27580	0.000030	0.064460	0.041812	3	3.106302
BEH2	0.000024	0.050523	0.036585	3	3.087132
DRIP2	0.000012	0.073171	0.076655	3	3.149838
EIL3	0.000006	0.052265	0.078397	3	3.130668
CHR4	0.000006	0.041812	0.043554	3	3.085372

## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)

	cor_betweenness_centrality	end_betweenness_centrality	cor_out_centrality	end_out_centrality	cor_in_centrality	end_in_centrality	centrality_count	centrality_sum
AT1G05710	0.837503	0.002241	0.832627	0.210526	0.209746	0.159148	6	8.251791
AT1G72210	0.785702	0.017866	0.358051	0.001253	0.241525	0.015038	6	7.419435
JAZ6	0.449238	0.000308	0.459746	0.001253	0.239407	0.028822	6	7.178774
MYB12	0.639291	0.000925	0.061441	0.001253	0.110169	0.045113	6	6.858191
AT5G57150	0.037672	0.011064	0.093220	0.110276	0.273305	0.111529	6	6.637067
MYB122	0.000216	0.001134	0.207627	0.208020	0.086864	0.114035	6	6.617896
IDD4	0.010796	0.000013	0.317797	0.036341	0.120763	0.038847	6	6.524556
LRP1	0.000045	0.000135	0.033898	0.003759	0.260593	0.016291	6	6.314722
PTF1	0.015874	0.001233	0.141949	0.001253	0.125000	0.028822	6	6.314131
ERF15	0.069380	0.000002	0.072034	0.005013	0.033898	0.026316	6	6.206642
AT3G23690	0.004665	0.000027	0.021186	0.018797	0.061441	0.047619	6	6.153735
SHL1	0.000004	0.000289	0.063559	0.017544	0.036017	0.018797	6	6.136211
AT1G63170	0.029414	0.000002	0.059322	0.011278	0.004237	0.012531	6	6.116784

## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	cor_betweenness_centrality	cor_out_centrality	cor_in_centrality	centrality_count	centrality_sum
AT2G38300	0.896209	0.103814	0.188559	3	4.188582
RGL3	0.630618	0.063559	0.078390	3	3.772567
SIGF	0.616944	0.006356	0.055085	3	3.678384
HMG	0.431457	0.004237	0.042373	3	3.478067
WRKY69	0.230087	0.052966	0.120763	3	3.403815
LBD39	0.060154	0.025424	0.031780	3	3.117358
IDD7	0.043228	0.273305	0.122881	3	3.439414
HSFB3	0.035086	0.103814	0.014831	3	3.153730
TGA3	0.015181	0.101695	0.019068	3	3.135944
MYB14	0.011583	0.023305	0.042373	3	3.077261
AT1G68070	0.000058	0.059322	0.048729	3	3.108109
TOC1	0.000058	0.088983	0.019068	3	3.108109
NF-YA4	0.000009	0.023305	0.019068	3	3.042382
FRS8	0.000004	0.027542	0.004237	3	3.031784
ZFN1	0.000004	0.044492	0.144068	3	3.188564

## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	end_betweenness_centrality	end_out_centrality	end_in_centrality	centrality_count	centrality_sum
MYB36	0.422861	0.998747	1.001253	3	5.422861
MYB74	0.403892	0.250627	0.718045	3	4.372564
bZIP58	0.145439	0.283208	0.091479	3	3.520126
chr31	0.136345	0.309524	0.151629	3	3.597498
HDG11	0.076425	0.002506	0.001253	3	3.080185
...	...	...	...	...	...
AT5G47790	0.000002	0.003759	0.005013	3	3.008774
TRB1	0.000002	0.010025	0.006266	3	3.016292
AT3G18870	0.000002	0.017544	0.018797	3	3.036342
NSI	0.000002	0.010025	0.006266	3	3.016292
AT3G10030	0.000002	0.018797	0.002506	3	3.021305

99 rows × 5 columns

## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)

	per_betweenness_centrality	pro_betweenness_centrality	xyl_betweenness_centrality	phl_betweenness_centrality	per_out_centrality	pro_out_centrality	xyl_out_centrality	phl_out_centrality	per_in_centrality	pro_in_centrality	xyl_in_centrality	phl_in_centrality	centrality_count	centrality_sum
AT3G43430	0.983399	0.991083	0.002998	0.941993	0.719626	0.264642	0.011029	0.113069	0.306075	0.290672	0.125000	0.095448	12	16.845034
MYB88	0.178522	0.365675	0.001343	0.096081	0.142523	0.752711	0.069853	0.258443	0.060748	0.867679	0.279412	0.152717	12	15.225707

## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	per_betweenness_centrality	per_out_centrality	per_in_centrality	centrality_count	centrality_sum
AT1G26790	0.966961	0.093458	0.341121	3	4.401541
IDD11	0.925491	0.037383	0.177570	3	4.140444
bZIP4	0.925141	0.137850	0.137850	3	4.200842
ICE1	0.895768	0.556075	0.114486	3	4.566329
ATL5	0.892162	0.261682	0.137850	3	4.291695
HB-2	0.864924	0.023364	0.074766	3	3.963055
LBD38	0.843228	0.210280	0.084112	3	4.137621
AT1G47570	0.813161	0.130841	0.067757	3	4.011759
LBD14	0.758645	0.032710	0.044393	3	3.835748
HB21	0.311749	0.581776	0.233645	3	4.127170
HDA3	0.211791	0.014019	0.032710	3	3.258520
ZFP7	0.129610	0.042056	0.070093	3	3.241760
SPL1	0.116368	0.032710	0.126168	3	3.275247
AT1G64620	0.102185	0.004673	0.044393	3	3.151251
MGP	0.093452	0.051402	0.193925	3	3.338780
CPUORF7	0.032103	0.014019	0.011682	3	3.057804
OFP1	0.016656	0.044393	0.081776	3	3.142824
AT5G23280	0.007797	0.025701	0.163551	3	3.197050
AT2G14880	0.006769	0.030374	0.056075	3	3.093217
WOX14	0.004394	0.025701	0.046729	3	3.076824
MAF4	0.003896	0.032710	0.035047	3	3.071653
AT2G35430	0.000640	0.030374	0.014019	3	3.045033
GATA16	0.000558	0.011682	0.021028	3	3.033268
NF-YA2	0.000547	0.009346	0.016355	3	3.026248
CDF3	0.000476	0.224299	0.046729	3	3.271504
ERF12	0.000208	0.214953	0.196262	3	3.411423
AT3G16350	0.000197	0.193925	0.067757	3	3.261879
AT2G20280	0.000027	0.002336	0.088785	3	3.091149
RAP2.2	0.000022	0.074766	0.042056	3	3.116844
AT4G31420	0.000005	0.025701	0.086449	3	3.112155

## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	pro_betweenness_centrality	pro_out_centrality	pro_in_centrality	centrality_count	centrality_sum
HB18	0.961256	0.184382	0.130152	3	4.275790
HAT3	0.937320	0.169197	0.125813	3	4.232330
VAL3	0.936876	0.049892	0.086768	3	4.073536
SHR	0.631835	0.023861	0.062907	3	3.718603
DOF1	0.576714	0.112798	0.049892	3	3.739404
HAT14	0.510153	0.084599	0.032538	3	3.627289
ETC1	0.193922	0.052061	0.052061	3	3.298043
REV	0.176398	0.418655	0.073753	3	3.668806
AT2G38090	0.106692	0.088937	0.019523	3	3.215151
CHR38	0.103409	0.039046	0.065076	3	3.207531
AT2G40200	0.074833	0.069414	0.043384	3	3.187631
AIF1	0.007889	0.049892	0.043384	3	3.101165
HAT9	0.005796	0.019523	0.117137	3	3.142455
MYB60	0.005640	0.088937	0.069414	3	3.163991
TCP20	0.005230	0.019523	0.023861	3	3.048614
AT1G69570	0.004876	0.021692	0.047722	3	3.074290
STO	0.003277	0.264642	0.084599	3	3.352518
AT1G28310	0.002174	0.086768	0.234273	3	3.323215
HSFB4	0.001962	0.071584	0.002169	3	3.075714
SHY2	0.001504	0.132321	0.062907	3	3.196732
BBX30	0.001438	0.008677	0.075922	3	3.086037
OBP4	0.000613	0.147505	0.069414	3	3.217533
AT3G50650	0.000141	0.004338	0.015184	3	3.019664
AT4G25410	0.000047	0.060738	0.086768	3	3.147553
GRP2	0.000014	0.108460	0.119306	3	3.227780
ARF19	0.000005	0.041215	0.049892	3	3.091111
HY5	0.000005	0.013015	0.049892	3	3.062911

## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	xyl_betweenness_centrality	xyl_out_centrality	xyl_in_centrality	centrality_count	centrality_sum
VND6	0.991562	0.205882	0.352941	3	4.550385
MYB83	0.984331	0.786765	0.305147	3	5.076243
OFP10	0.975920	0.183824	0.312500	3	4.472243
AT3G60580	0.968825	0.224265	0.360294	3	4.553383
AT1G68200	0.964388	0.433824	0.268382	3	4.666594
AT1G66810	0.959586	0.790441	0.341912	3	5.091939
ZHD3	0.953223	0.757353	0.253676	3	4.964253
HB31	0.937581	0.106618	0.198529	3	4.242728
BEE2	0.904018	0.143382	0.187500	3	4.234901
MYB52	0.897371	0.665441	0.213235	3	4.776047
AT1G24040	0.893423	0.202206	0.198529	3	4.294158
BBX31	0.888051	0.091912	0.250000	3	4.229963
AT2G20100	0.882326	0.220588	0.191176	3	4.294091
MMD1	0.871825	0.055147	0.183824	3	4.110796
VND1	0.844869	0.202206	0.268382	3	4.315457
VND7	0.792273	0.841912	0.389706	3	5.023890
LBD31	0.788013	0.580882	0.231618	3	4.600513
AT4G16610	0.784865	0.121324	0.117647	3	4.023836
MYB85	0.780592	0.485294	0.345588	3	4.611474
AT5G18090	0.759049	0.073529	0.264706	3	4.097284
VND5	0.692384	0.783088	0.227941	3	4.703413
XND1	0.690552	0.095588	0.459559	3	4.245699
ASL9	0.662565	0.327206	0.386029	3	4.375800
PIF4	0.561130	0.238971	0.106618	3	3.906718
IAA6	0.267853	0.220588	0.279412	3	3.767853
MYB46	0.265181	0.863971	0.955882	3	5.085034
VND4	0.229745	0.834559	0.415441	3	4.479745
AT3G49930	0.133004	0.036765	0.069853	3	3.239622
AT5G25470	0.076582	0.150735	0.091912	3	3.319229
MYB99	0.076148	0.040441	0.235294	3	3.351883
AT3G22560	0.054645	0.216912	0.073529	3	3.345086
HB34	0.054496	0.091912	0.147059	3	3.293466
MYB97	0.048269	0.047794	0.033088	3	3.129151
GATA6	0.046926	0.113971	0.102941	3	3.263838
GIF3	0.039274	0.205882	0.084559	3	3.329716
HB23	0.032722	0.022059	0.062500	3	3.117281
AT1G26590	0.027933	0.018382	0.113971	3	3.160286
MYB64	0.020987	0.022059	0.025735	3	3.068781
AT1G24610	0.020349	0.161765	0.069853	3	3.251967
SHP1	0.018097	0.113971	0.069853	3	3.201921
AT5G46910	0.004029	0.389706	0.161765	3	3.555500
AT5G09460	0.003066	0.080882	0.099265	3	3.183213
GT-1	0.001099	0.117647	0.125000	3	3.243746
PHR1	0.001004	0.003676	0.058824	3	3.063504
GBF3	0.000841	0.003676	0.099265	3	3.103782
ERF9	0.000570	0.036765	0.007353	3	3.044687
JMJ14	0.000312	0.095588	0.084559	3	3.180459
RHC2A	0.000244	0.058824	0.044118	3	3.103185
AT1G04990	0.000244	0.121324	0.139706	3	3.261274
IAA10	0.000190	0.165441	0.051471	3	3.217102
ABF2	0.000136	0.069853	0.025735	3	3.095724
AT1G05120	0.000095	0.007353	0.117647	3	3.125095
BRH1	0.000054	0.102941	0.051471	3	3.154466
HK2	0.000054	0.117647	0.073529	3	3.191231
ARF1	0.000014	0.069853	0.136029	3	3.205896

## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)

	phl_betweenness_centrality	phl_out_centrality	phl_in_centrality	centrality_count	centrality_sum
AT3G12730	0.971001	0.870778	0.113069	3	4.954848
AT2G03500	0.951661	0.679883	0.146843	3	4.778386
GATA20	0.947724	0.071953	0.058737	3	4.078414
bHLH11	0.934720	0.008811	0.017621	3	3.961151
NAC057	0.916356	0.502203	0.142438	3	4.560996
DOF2.4	0.905453	0.089574	0.054332	3	4.049359
MBF1A	0.885024	0.060206	0.102790	3	4.048020
BHLH101	0.790265	0.041116	0.232012	3	4.063393
REM22	0.451963	0.001468	0.024963	3	3.478395
AGL15	0.291155	0.089574	0.246696	3	3.627425
APRR2	0.277615	0.030837	0.016153	3	3.324605
NAC020	0.276004	0.110132	0.167401	3	3.553537
NAC086	0.236285	0.020558	0.076358	3	3.333202
DAR2	0.132586	0.569750	0.123348	3	3.825685
SCL27	0.046741	0.005874	0.016153	3	3.068768
AT5G63700	0.025067	0.199706	0.256975	3	3.481748
AT1G49560	0.004341	0.107195	0.008811	3	3.120346
SVP	0.001486	0.095448	0.185022	3	3.281956
HMGB1	0.001153	0.070485	0.039648	3	3.111285
AT3G52270	0.000499	0.001468	0.007342	3	3.009309
BPC3	0.000218	0.004405	0.093979	3	3.098603
ILR3	0.000140	0.024963	0.064611	3	3.089715
AS1	0.000117	0.007342	0.008811	3	3.016269
FLP	0.000011	0.030837	0.077827	3	3.108675
ZAP1	0.000004	0.032305	0.004405	3	3.036715

Search for individual genes

gene = 'SHR'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	per_degree_centrality	per_out_centrality	per_in_centrality	per_closeness_centrality	per_eigenvector_centrality	pro_degree_centrality	pro_out_centrality	pro_in_centrality	pro_betweenness_centrality	pro_closeness_centrality	pro_eigenvector_centrality
SHR	1.0	0.060748	0.037383	0.023364	0.000513	0.019181	0.086768	0.023861	0.062907	0.631835	0.000413	0.038122

gene = 'BLJ'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	end_degree_centrality	end_out_centrality	end_in_centrality	end_betweenness_centrality	end_closeness_centrality	end_eigenvector_centrality
BLJ	1.0	0.322055	0.135338	0.186717	0.001074	0.000399	0.113638

gene = 'JKD'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	cor_degree_centrality	cor_out_centrality	cor_in_centrality	cor_closeness_centrality	cor_eigenvector_centrality	end_degree_centrality	end_out_centrality	end_in_centrality	end_betweenness_centrality	end_closeness_centrality	end_eigenvector_centrality
JKD	1.0	0.258475	0.038136	0.220339	0.000534	0.088457	0.196742	0.072682	0.12406	0.006877	0.000378	0.089774

gene = 'RVN'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	end_degree_centrality	end_out_centrality	end_in_centrality	end_betweenness_centrality	end_closeness_centrality	end_eigenvector_centrality
RVN	1.0	0.16792	0.12782	0.0401	0.000039	0.000293	0.086271

gene = 'BIB'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	end_degree_centrality	end_out_centrality	end_in_centrality	end_betweenness_centrality	end_closeness_centrality	end_eigenvector_centrality
BIB	1.0	0.097744	0.051378	0.046366	0.000016	0.000321	0.061238

gene = 'IME'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

gene = 'MYB66'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

gene = 'GL2'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]

	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality
GL2	1.0	0.334495	0.191638	0.142857	0.972686	0.000502	0.077748

tf_spec.to_csv('TF_GRN_centrality_t7-t9_zscore3.csv', index=True)